CN117493775B

CN117493775B - Relative navigation method and device of data chain, electronic equipment and storage medium

Info

Publication number: CN117493775B
Application number: CN202311841074.3A
Authority: CN
Inventors: 董飞; 杨东森; 王鹏; 刘巍巍
Original assignee: Beijing Hualongtong Technology Co ltd
Current assignee: Beijing Hualongtong Technology Co ltd
Priority date: 2023-12-29
Filing date: 2023-12-29
Publication date: 2024-05-14
Anticipated expiration: 2043-12-29
Also published as: CN117493775A

Abstract

The embodiment of the application provides a relative navigation method, a relative navigation device, electronic equipment and a storage medium of a data link, and relates to the field of radio relative navigation, wherein the method comprises the following steps: firstly, acquiring measurement information of a platform sensor at the current moment; preprocessing the original state measurement value in the current moment measurement information to obtain a state measurement value at the current moment; and finally, determining the state target value of the platform at the current moment according to the state measurement value at the current moment, the state target value of the platform at the last moment and the covariance matrix at the last moment based on the system-level sequential Kalman filtering model, so as to be used for platform navigation. According to the application, the information measured by the platform sensor is subjected to self-adaptive preprocessing, so that a more accurate state measurement value is obtained, and further, a state target value obtained according to the state measurement value is more accurate, thereby improving the precision of the platform relative navigation information.

Description

Relative navigation method and device of data chain, electronic equipment and storage medium

Technical Field

The present application relates to the field of radio relative navigation, and in particular, to a relative navigation method and apparatus for a data link, an electronic device, and a storage medium.

Background

Along with the high-speed development of relative navigation technology, the application field of the navigation system is also expanded, and the navigation system is widely and successfully applied to various fields such as aviation, aerospace, geodetic measurement and monitoring.

The relative navigation technique is that each member in the relative navigation system network periodically transmits accurate participation positioning and identification (PPLI) message information in the time slot allocated to the relative navigation system network by utilizing a data chain under the condition that the clocks of the members are nearly synchronous. The in-network member who receives PPLI the message calculates PPLI the time TOA of arrival of the message by using the time of itself and the navigation source, thereby calculating the distance between the navigation source and itself. The system selects a navigation source with good geometric relationship through logic, and calculates the position of the navigation source in a relative coordinate system by combining sensor navigation information provided by a platform of the system.

At present, the navigation information of the platform in the relative navigation of the data link has a plurality of types and large variation, so that the observed quantity of the navigation information obtained by the platform sensor has low continuity and poor reliability, and the state values of the position and the like of the platform in the relative coordinate system cannot be accurately calculated according to the observed quantity.

Disclosure of Invention

In view of the above, the embodiments of the present application provide a relative navigation method, apparatus, electronic device and storage medium for a data link, so as to improve the accuracy of relative navigation information.

In one aspect, the present application provides a method for relative navigation of a data link, comprising:

Acquiring measurement information of a platform sensor at the current moment, wherein the measurement information comprises an original state measurement value of the platform at the current moment;

Preprocessing the original state measurement value at the current moment to obtain a state measurement value at the current moment; the preprocessing at least comprises at least one of data validity processing, accuracy processing, noise reduction processing and diagonalization processing;

Based on a system-level sequential Kalman filtering model, determining a state target value of the platform at the current moment according to the state measurement value at the current moment, the state target value of the platform at the last moment and a covariance matrix at the last moment, so as to be used for navigation of the platform.

In one possible implementation of the present application, the measurement information carries a sensor class identifier, and the data validity processing includes:

determining the category of the original state measurement value according to the sensor category identification carried by the measurement information;

Based on a preset data validity comparison table, determining a data validity interval corresponding to the original state measurement value according to the category, wherein the data validity comparison table represents the corresponding relation between the category of the original state measurement value and the data validity interval;

And if the original state measurement value does not meet the data validity interval, setting the state measurement value at the current moment to be equal to the original state measurement value at the last moment.

In one possible implementation manner of the present application, the platform sensor includes at least a first sensor and a second sensor, the first sensor and the second sensor are sensors of the same class, and the accuracy processing includes:

calculating a first user ranging accuracy of the first sensor according to a first user ranging accuracy factor based on a user ranging accuracy formula, calculating a second user ranging accuracy of the second sensor according to a second user ranging accuracy factor, wherein the measurement information comprises the first user ranging accuracy factor of the first sensor and the second user ranging accuracy factor of the second sensor;

according to the first user ranging precision and the second user ranging precision, respectively calculating to obtain a first weight of a first original state measurement value and a second weight of a second original state measurement value, wherein the original state measurement value comprises the first original state measurement value and the second original state measurement value;

And calculating the state measurement value at the current moment according to the first original state measurement value, the first weight, the second original state measurement value and the second weight.

In one possible implementation manner of the present application, the noise reduction processing includes:

Calculating to obtain measurement prediction noise at the current moment according to the prediction covariance matrix at the current moment, the original state measurement value at the current moment and the measurement prediction value at the current moment;

And updating the original measurement noise covariance matrix according to the maximum value and the minimum value of the original measurement noise covariance matrix of the original state measurement value at the current moment and the magnitude relation of the measurement prediction noise at the current moment to obtain the state measurement value at the current moment.

In one possible implementation manner of the present application, the updating the original measurement noise covariance matrix according to the relation between the maximum value and the minimum value of the original measurement noise covariance matrix of the original state measurement value at the current moment and the magnitude of the measurement prediction noise at the current moment to obtain the state measurement value at the current moment includes:

If the measurement prediction noise at the current moment is larger than the maximum value of the original measurement noise covariance matrix, updating the original measurement noise covariance matrix to the maximum value to obtain the measurement noise covariance matrix at the current moment and the state measurement value at the current moment;

and if the measurement prediction noise at the current moment is not greater than the minimum value of the original measurement noise covariance matrix, updating the original measurement noise covariance matrix according to the self-adaptive factor at the current moment to obtain the measurement noise covariance matrix at the current moment and the state measurement value at the current moment.

In one possible implementation of the present application, the measurement information includes sub-measurement information of a plurality of sensors, and the current raw state measurement value includes a plurality of raw state sub-measurement values; the diagonalization process includes:

diagonalizing the measurement noise covariance matrix in each original state sub-measurement value at the current moment to obtain a state sub-measurement value at the current moment, wherein the state measurement value at the current moment comprises a plurality of state sub-measurement values at the current moment.

In one possible implementation manner of the present application, the determining, based on the system-level sequential kalman filter model, the state target value of the platform at the current time according to the state measurement value at the current time, the state target value of the platform at the last time, and the covariance matrix at the last time includes:

based on a system state model in the Kalman filtering model, obtaining a state predicted value of the platform at the current moment according to a state target value of the platform at the last moment, and obtaining a predicted covariance matrix at the current moment according to a covariance matrix at the last moment;

Determining a filtering gain matrix at the current moment according to a measurement noise covariance matrix at the current moment in the state measurement values at the current moment and a prediction covariance matrix at the current moment based on a system measurement model in the Kalman filtering model;

and determining a state target value of the platform at the current moment according to the state predicted value of the current moment, the filtering gain matrix at the current moment and the state measurement value at the current moment.

In one aspect, the present application provides a relative navigation device for a data link, comprising:

The first acquisition module is used for acquiring measurement information of the platform sensor at the current moment, wherein the measurement information comprises an original state measurement value of the platform at the current moment;

the first preprocessing module is used for preprocessing the original state measurement value at the current moment to obtain the state measurement value at the current moment; the preprocessing at least comprises at least one of data validity processing, accuracy processing, noise reduction processing and diagonalization processing;

The first determining module is configured to determine, based on a system-level sequential kalman filter model, a state target value of the platform at a current moment according to the state measurement value at the current moment, a state target value of the platform at a last moment, and a covariance matrix of the last moment, so as to be used for navigation of the platform.

In one aspect, an embodiment of the present application provides an electronic device, where the electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and where the processor implements steps in a relative navigation method of a data link as described above when the processor executes the computer program.

In one aspect, the present application provides a computer readable storage medium having a computer program stored thereon, which when executed by a processor performs steps in a relative navigation method of a data link as described above.

The embodiment of the application provides a relative navigation method, a relative navigation device, electronic equipment and a storage medium of a data chain. According to the application, the information measured by the platform sensor is subjected to self-adaptive preprocessing, so that the accuracy and reliability of relative navigation information are improved, and the relative navigation accuracy and reliability are further improved.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings can be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1a is a schematic diagram of a first scenario of a relative navigation system of a data link according to an embodiment of the present application.

Fig. 1b is a schematic diagram of a second scenario of a relative navigation system of a data link according to an embodiment of the present application.

Fig. 2 is a schematic flow chart of a relative navigation method of a data link according to an embodiment of the present application.

Fig. 3 is a first filtering flow framework provided in an embodiment of the present application.

Fig. 4 is a second filtering flow framework provided in an embodiment of the present application.

Fig. 5 is a second flowchart of a relative navigation method of a data link according to an embodiment of the present application.

Fig. 6 is a diagram of positioning results of a conventional method according to an embodiment of the present application.

Fig. 7 is a diagram of positioning results of the method according to the present application according to an embodiment of the present application.

Fig. 8 is a third flowchart of a relative navigation method of a data link according to an embodiment of the present application.

Fig. 9 is a flowchart of overall processing of a system according to an embodiment of the present application.

Fig. 10 is a schematic structural diagram of a relative navigation device of a data link according to an embodiment of the present application.

Fig. 11 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

Embodiments of the present application will be described in detail below with reference to the accompanying drawings.

Other advantages and effects of the present application will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present application with reference to specific examples. It will be apparent that the described embodiments are only some, but not all, embodiments of the application. The application may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present application. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.

In the present application, the term "exemplary" is used to mean "serving as an example, instance, or illustration. Any embodiment described as "exemplary" in this disclosure is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is presented to enable any person skilled in the art to make and use the application. In the following description, details are set forth for purposes of explanation. It will be apparent to one of ordinary skill in the art that the present application may be practiced without these specific details. In other instances, well-known structures and processes have not been described in detail so as not to obscure the description of the application with unnecessary detail. Thus, the present application is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

The execution body of the relative navigation method of the data link in the embodiment of the present application may be a relative navigation device of the data link provided in the embodiment of the present application, or different types of electronic devices such as a server device, a physical host or a User Equipment (UE) integrated with the relative navigation device of the data link, where the relative navigation device of the data link may be implemented in a hardware or software manner, and the UE may specifically be a terminal device such as a smart phone, a tablet computer, a notebook computer, a palm computer, a desktop computer, or a Personal digital assistant (Personal DIGITAL ASSISTANT, PDA).

The electronic device may be operated in a single operation mode, or may also be operated in a device cluster mode.

Referring to fig. 1a, fig. 1a is a schematic diagram of a first scenario of a relative navigation system of a data link according to an embodiment of the present application. The relative navigation system of the data link may include the electronic device 100, and a relative navigation apparatus of the data link is integrated in the electronic device 100. For example, the electronic device may obtain measurement information of the platform sensor at the current time; preprocessing the original state measurement value at the current moment in the measurement information to obtain a state measurement value at the current moment; and determining a state target value of the platform at the current moment according to the state measurement value at the current moment, the state target value of the platform at the last moment and the covariance matrix at the last moment based on the system-level sequential Kalman filtering model, so as to be used for platform navigation.

In addition, as shown in fig. 1a, the relative navigation system of the data link further includes a sensor 200 for acquiring state information of the platform at each moment, wherein the state information of the platform at each moment includes, but is not limited to, latitude, longitude, altitude, speed, roll angle, pitch angle, heading angle and the like.

As shown in fig. 1b, fig. 1b is a schematic diagram of a second scenario of a relative navigation system of a data link provided by an embodiment of the present application, where a platform is an aircraft 400, and satellite navigation sensors are loaded on the aircraft 400, and the satellite navigation sensors obtain measurement information of the aircraft 400 according to positioning information of satellites 300 and state information thereof.

It should be noted that, the schematic diagrams of the scenario of the relative navigation system of the data link shown in fig. 1a and fig. 1b are only an example, and the relative navigation system of the data link and the scenario described in the embodiments of the present application are for more clearly describing the technical solution of the embodiments of the present application, and do not constitute a limitation on the technical solution provided by the embodiments of the present application, and as the relative navigation system of the data link evolves and new service scenarios appear, those skilled in the art can know that the technical solution provided by the embodiments of the present application is equally applicable to similar technical problems.

In the following, a method for relative navigation of a data link provided in the embodiment of the present application is described, where an electronic device is used as an execution body, and in order to simplify and facilitate description, the execution body will be omitted in the subsequent method embodiments.

Referring to fig. 2, fig. 2 is a first flowchart of a relative navigation method of a data link according to an embodiment of the present application, which is applied to the electronic device, and the relative navigation method of the data link includes:

step 201, obtaining measurement information of a platform sensor at the current moment.

The measurement information comprises an original state measurement value of the platform at the current moment.

In the present application, platforms include, but are not limited to, sensor-loaded platforms for aircraft, automobiles, and the like.

In the present application, the types of sensors include, but are not limited to, satellite navigation, inertial navigation, integrated navigation, tacon, and the like.

In the present application, the raw state measurement value at the current time may be latitude, longitude, altitude, speed, roll angle, pitch angle, heading angle, etc. of the platform, and the specific raw state measurement value is not limited herein.

The measurement information of the platform sensor at the current moment may be information obtained by directly measuring the sensor, or may be information obtained by a series of processes performed on the directly measured information by the sensor, and the specific situation is determined according to the function of the sensor, which is not limited herein.

Step 202, preprocessing the original state measurement value in the measurement information of the current moment to obtain the state measurement value of the current moment.

The current time is t, t=k, k is greater than or equal to 1, and for convenience of description, the default k time is the current time in the application.

Wherein the preprocessing includes at least one of data validity processing, accuracy processing, noise reduction processing, and diagonalization processing.

The preprocessing may be any one of data validity processing, accuracy processing, noise reduction processing, and diagonalization processing, or any plurality of the same, which is not limited herein. And the front-back sequence of the pretreatment is not limited, so that the actual application scene is satisfied.

In one embodiment, the measurement information carries a sensor class identifier, and the data validity process includes: determining the category of the original state measurement value according to the sensor category identification carried by the measurement information; based on a preset data validity comparison table, determining a data validity interval corresponding to the original state measurement value according to the category, wherein the data validity comparison table represents the corresponding relation between the category of the original state measurement value and the data validity interval; if the original state measurement value does not meet the data validity interval, setting the state measurement value at the current moment to be equal to the original state measurement value at the last moment.

The data validity processing method can comprise the following steps:

Step one: and determining the category of the original state measurement value according to the sensor category identification carried by the measurement information.

Wherein the class identification of the sensor characterizes the class of the sensor, for example: satellite navigation, inertial navigation, integrated navigation, takang and the like all have different identifications, wherein the category identifications can be set before the sensor leaves the factory or set by a user after the sensor leaves the factory, and the category identifications are not limited.

Because the state quantities measured by the sensors of different types are different, and the measurement information of the platform sensor can carry the sensor type identification, the type of the original state measurement value can be determined according to the sensor type identification.

For example, the satellite navigation mainly measures the position, the angle of the inertial navigation mainly measures, etc., the identification of the satellite navigation sensor is 001, the identification of the inertial navigation sensor is 002, and when the sensor identification carried by the measurement information measured by the platform sensor is 001, the corresponding category of the measurement value of the original state is longitude and latitude.

Step two: based on a preset data validity comparison table, determining a data validity interval corresponding to the original state measurement value according to the category of the original state measurement value.

The data validity comparison table characterizes the corresponding relation between the category of the original state measurement value and the data validity interval.

After determining the category of the original state measurement value in the previous step, the data validity interval corresponding to the original state measurement value can be determined according to the category of the original state measurement value based on a preset data validity comparison table.

Table 1 data validity comparison table

Step three: if the original state measurement value does not meet the data validity interval, setting the state measurement value at the current moment to be equal to the original state measurement value at the last moment.

If the original state measurement value is not in the range of the data validity interval, the state measurement value at the current moment is equal to the original state measurement value at the last moment, namely the state measurement value at the current moment is equal to the original state measurement value at the last moment; if the original state measurement value is within the range of the data validity interval, the state measurement value at the current moment is unchanged, namely the original state measurement value at the current moment is equal to the state measurement value at the current moment.

For example: if the longitude value in the original state measurement value at the current moment isWhen the longitude value in the state measurement value at the current moment is equal to the longitude value/>, in the original state measurement value at the current moment; If the latitude value in the original state measurement value at the current moment is/>And if so, the latitude value in the state measurement value at the current moment is equal to the latitude value in the original state measurement value at the previous moment.

According to the application, the data validity is carried out on the original state measurement value, some original state measurement values with larger errors are filtered, the state measurement value at the current moment with higher validity is obtained, and the reliability of relative navigation is further improved.

In an actual application scenario, a plurality of sensors with the same function but different models or manufacturers are generally used to measure the same state, for example: both sensor 1 and sensor 2 are measuring the speed of movement of the platform, but since sensor 1 and sensor 2 are from different manufacturers, they differ in accuracy of the measured data. The application provides a solution to this problem.

In the present application, the categories of sensors are used to distinguish the functions of the sensors, e.g., sensors of the same function are the same in their categories.

In one embodiment, the platform sensor includes at least a first sensor and a second sensor, the first sensor and the second sensor being of the same class of sensors, the accuracy process comprising: calculating a first user ranging accuracy of the first sensor according to the first user ranging accuracy factor based on the user ranging accuracy formula, calculating a second user ranging accuracy of the second sensor according to the second user ranging accuracy factor, and measuring information including the first user ranging accuracy factor of the first sensor and the second user ranging accuracy factor of the second sensor; according to the first user ranging precision and the second user ranging precision, respectively calculating to obtain a first weight of a first original state measurement value and a second weight of a second original state measurement value, wherein the original state measurement value comprises the first original state measurement value and the second original state measurement value; and calculating to obtain a state measurement value at the current moment according to the first original state measurement value, the first weight, the second original state measurement value and the second weight.

The accuracy processing method can comprise the following steps:

Step one: based on the user ranging accuracy formula, calculating a first user ranging accuracy of the first sensor according to a first user ranging accuracy factor in the measurement information, and calculating a second user ranging accuracy of the second sensor according to a second user ranging accuracy factor in the measurement information.

The measurement information comprises a first user ranging precision factor of the first sensor and a second user ranging precision factor of the second sensor.

Since the measurement information given by different positioning sensors is given out simultaneously with the user ranging accuracy factor N, the user ranging accuracy URA is estimated according to the value of N by the following formula.

(Equation 1)

For example: the first user ranging accuracy factor of the first sensor is 6, the first user ranging accuracy24, The second user ranging accuracy of the second sensor is 8, the second user ranging accuracy26.

Step two: and according to the first user ranging precision and the second user ranging precision, calculating to obtain a first weight of a first original state measurement value and a second weight of a second original state measurement value in the original state measurement values.

Then according to the user ranging accuracyThe value is weighted according to equation 2:

(equation 2), wherein,/> Weight representing the xth raw state measurement,/>User ranging accuracy representing the ith raw state measurement value,Representing the user ranging accuracy of the xth sensor.

The first weight of the first initial state measurement value in the initial state measurement values can be calculated by the formula 2Second weight/>, second raw state measure。

Step three: and calculating to obtain a state measurement value at the current moment according to the first original state measurement value, the first weight, the second original state measurement value and the second weight.

Thus, the state measurement value at the current time can be calculated according to the above formula 3.

The application selects corresponding sensor error characteristics according to different sensor information, and gives corresponding weight values according to actual use environments, thereby improving the precision of state measurement values and further improving the precision of relative navigation.

Because the sensor can generate certain measurement noise when in practical application, and the measurement noise can bring certain errors to the original state measurement value, noise reduction processing is needed to be carried out on the original state measurement value, and a state measurement value with higher accuracy is obtained.

Because the measurement noise is reflected as the value of the measurement noise covariance matrix in the Kalman preprocessing filter, the noise reduction processing is performed on the original state measurement value, and the size of the original measurement noise covariance matrix in the original state measurement value is actually adjusted.

In one embodiment, the noise reduction process includes: calculating to obtain measurement prediction noise at the current moment according to the prediction covariance matrix at the current moment, the original state measurement value at the current moment and the measurement prediction value at the current moment; and updating the original measurement noise covariance matrix according to the relation between the maximum value and the minimum value of the original measurement noise covariance matrix of the original state measurement value at the current moment and the measurement prediction noise at the current moment to obtain the state measurement value at the current moment.

The noise reduction processing method may include the steps of:

step one: and calculating to obtain the measurement prediction noise at the current moment according to the prediction covariance matrix at the current moment, the original state measurement value at the current moment and the measurement prediction value at the current moment.

An independent sensor measurement information preprocessing filter is first established. The preprocessing filter is built up according to a standard kalman filter.

Then, the measurement prediction noise at the current moment can be calculated by the formula 4:

(equation 4), wherein,/> Represents time k/>Measurement of prediction noise,/>To measure prediction error,/>For measuring covariance matrix,/>For time k/>A measurement matrix corresponding to each state measurement value, wherein/>Is known as a measurement matrix,/>Covariance matrix/>, which can be measured according to the ith raw state quantity at time k-1, based on a system state model in a Kalman filtering modelThe product can be obtained by the method,Is the initial parameters of the system or is calculated according to the initial parameters of the system.

(Equation 5)/(Represents the i-th raw state measurement value at k time,/>Representing the i-th original state predicted value at the k moment.

Since, as can be seen from equations 4 and 5, the predicted covariance matrix P _k,k-1 at the current time, the raw state measurement value Z _k at the current time, and the measurement prediction value at the current timeCalculating the measurement prediction noise/>, at the current moment。

Step two: and updating the original measurement noise covariance matrix according to the relation between the maximum value and the minimum value of the original measurement noise covariance matrix of the original state measurement value at the current moment and the measurement prediction noise at the current moment to obtain the state measurement value at the current moment.

Since the state measurement value=the actual measurement value+the measurement noise, where the measurement noise v (k) ∈ (0, R _k), the noise reduction of the current-time original state measurement value is actually to adjust the measurement noise in the current-time original state measurement value, and since the measurement noise v (k) is in the normal distribution of (0, R _k), changing the measurement noise v (k) is to change the covariance matrix R _k of the measurement noise v (k).

(Equation 6), wherein/>For/>Time of day/>Measurement noise array of state measurement values subjected to noise reduction processing,/>、/>Respectively represent the/>Upper and lower limits of measured noise variance matrix for each measurement,/>Is an adaptive factor of k time,/>Is the adaptive factor at time k-1. Wherein/>(Equation 7)/(Called fading factor, get/>The value of b is determined according to practical operation experience, and b is 0.9 in the application.

As shown in equation 6, the original measurement noise covariance matrix can be updated according to the maximum and minimum values of the original measurement noise covariance matrix of the original state measurement value at the current time and the magnitude relation of the measurement prediction noise at the current time to obtain the state measurement value at the current time.

In one embodiment, updating the original measurement noise covariance matrix according to the maximum value and the minimum value of the original measurement noise covariance matrix of the original state measurement value at the current moment and the magnitude relation of the measurement prediction noise at the current moment to obtain the state measurement value at the current moment includes: if the measurement prediction noise at the current moment is larger than the maximum value of the original measurement noise covariance matrix, updating the original measurement noise covariance matrix to the maximum value to obtain the measurement noise covariance matrix at the current moment and the state measurement value at the current moment; if the measurement prediction noise at the current moment is not greater than the minimum value of the original measurement noise covariance matrix, updating the original measurement noise covariance matrix according to the self-adaptive factor at the current moment to obtain the measurement noise covariance matrix at the current moment and the state measurement value at the current moment.

As can be seen from equation 6, if the current time is measured to predict noiseGreater than the maximum value/>, of the original measured noise covariance matrixThe measurement noise covariance matrix at the current moment in the state measurement values at the current moment is/>。

If the measurement of the current moment predicts noiseLess than the minimum value/>, of the original measured noise covariance matrixThe measurement noise covariance matrix at the current moment in the state measurement values at the current moment is/>; Otherwise, the measurement noise covariance matrix of the current moment in the state measurement value of the current moment is。

According to the application, the noise reduction processing is carried out on the original state measurement value in advance, so that the noise of the state measurement value at the current moment is reduced, and the accuracy of relative navigation is further improved.

In one embodiment, the measurement information includes sub-measurement information of a plurality of sensors, and the raw state measurement value at the current time includes a plurality of raw state sub-measurement values; a diagonalization process comprising: diagonalizing the measurement noise covariance matrix in each original state sub-measurement value at the current moment to obtain a state sub-measurement value at the current moment, wherein the state measurement value at the current moment comprises a plurality of state sub-measurement values at the current moment.

In order to effectively avoid the disadvantage of resolving the high-order filter, the system-level sequential Kalman filtering model of the application decomposes the measurement value update into N sub-measurement value updates, and respectively performs block filtering processing on the state quantities of different sensor navigation information, for example: (equation 8), where noise/> And/>Are not related to each other,/>Is a state measurement value.

If the measured noise covariance matrix corresponding to each sub-measured value is not a diagonal matrix, the measured noise covariance matrix in each original state sub-measured value at the current moment needs to be diagonalized.

The detailed method of the diagonalization treatment is as follows:

first, for the measurement noise covariance matrix in the state sub-measurement value The following trigonometric decomposition is performed: (equation 9), wherein/> Is a non-singular upper triangular (or lower triangular) matrix. To/>Meanwhile, the measurement equation is multiplied left and right to obtain/>(Equation 10), equation 10 is abbreviated as/>Wherein/>，/>，/>，/>The measurement noise variance matrix of the state sub-measurement value at the current moment is。

According to the application, different kinds of sensor measurement information are subjected to classification pretreatment, a system-level sequential Kalman filtering model is established, and the asymmetric situation after the measurement noise covariance matrix time variation is considered, and compared with a conventional Kalman filtering model, the method and the device perform N-time recursive least square estimation, so that the defect of high-order filter calculation is effectively avoided, the stability of numerical calculation and the robustness of a filtering frame are enhanced, and the requirement of low time delay in the relative navigation application of a data link is greatly met.

Step 203, determining a state target value of the platform at the current moment according to the state measurement value at the current moment, the state target value of the platform at the last moment and the covariance matrix at the last moment based on the system-level sequential kalman filter model, so as to be used for platform navigation.

In one embodiment, determining the state target value of the platform at the current time based on the system-level sequential kalman filter model according to the state measurement value at the current time, the state target value of the platform at the last time, and the covariance matrix at the last time includes: based on a system state model in the Kalman filtering model, obtaining a state predicted value of the platform at the current moment according to a state target value of the platform at the last moment, and obtaining a predicted covariance matrix at the current moment according to a covariance matrix at the last moment; determining a filtering gain matrix at the current moment based on a system measurement model in the Kalman filtering model according to a measurement noise covariance matrix at the current moment in the state measurement values at the current moment and a prediction covariance matrix at the current moment; and determining a state target value of the platform at the current moment according to the state predicted value at the current moment, the filtering gain matrix at the current moment and the state measurement value at the current moment.

Firstly, based on a system state model in a Kalman filtering model, calculating a state predicted value of a platform at a current moment according to a state target value of the platform at a previous moment, and calculating a predicted covariance matrix at the current moment according to a covariance matrix at the previous moment.

A system state model in the kalman filter model: Wherein, the method comprises the steps of, wherein, Is a systematic noise vector,/>Matrix allocation for system noise,/>For/>Real state target value of time,/>Is a known system state transition matrix,/>The predicted value of the real state at the time k. /(I)

Optimal one-step prediction result in the filtering model: (equation 11), (Equation 12), wherein,/>For/>Time state target value,/>A state predicted value at the moment k; /(I)For/>Covariance matrix of time,/>For the prediction covariance matrix at time k,/>For the system noise vector/>Is a covariance matrix of (a).

From the above equation 11, the state target value of the platform at the previous time can be calculatedSubstituting the predicted value into the formula 11 to obtain the state predicted value/>, of the platform at the current moment; The covariance matrix/>, according to the last momentSubstituting the prediction covariance matrix into the formula 12 to obtain the prediction covariance matrix/>。

And determining a filtering gain matrix at the current moment based on the measurement noise covariance matrix at the current moment in the state measurement values at the current moment and the prediction covariance matrix at the current moment.

Wherein, the system in the Kalman filtering model measures the model: Wherein/> For the state measurement value at time k,/>For the real state target value at time k,/>Is the measurement noise at the moment k, wherein the measurement noise v (k) is in the normal distribution of (0, R _k)/>For the k moment measurement matrix in the system parameters,/>To measure noise/>And measuring a noise covariance matrix correspondingly.

Due to(Equation 13), wherein/>For the filtering gain matrix,/>Is the prediction covariance matrix at time k.

Thus, the measurement noise covariance matrix at the current momentAnd a prediction covariance matrix for the current timeIs brought into a formula 13 to obtain a filtering gain matrix/>, at the current moment。

And finally, determining a state target value of the platform at the current moment according to the state predicted value at the current moment, the filter gain matrix at the current moment and the state measurement value at the current moment.

(Equation 14), wherein/>Is the state target value at time k,/>Is the state measurement value at the time k.

Thus, the state prediction value at the current time is calculatedFilter gain matrix/>, at the current momentState measurement value at the present time/>Substituting the target value into the formula 14 to calculate the state target value/>, of the platform at the current moment。

As shown in fig. 3, the process of determining the state best estimate at the current time in the present application can be divided into two stages, one is prediction extrapolation and one is measurement update.

The step 202 is a preprocessing stage, which is mainly to preprocess the original state measurement value to obtain a more accurate state measurement value.

The step 203 is a prediction extrapolation stage and a measurement update stage, wherein the prediction extrapolation stage predicts an optimal one-step predicted value at the current time by using the system model and a state target value at the previous time, and predicts a predicted covariance matrix at the current time by using the system model and a covariance matrix at the previous time.

In the measurement updating stage, the optimal one-step predicted value and the covariance matrix thereof at the current moment obtained in the system measurement model and the prediction extrapolation stage are used, and the state target value at the current moment is regulated by calculating the filtering gain matrix and the difference value between the system measurement value and the predicted value so as to achieve a more accurate result.

The embodiment of the application provides a relative navigation method of a data chain, which comprises the steps of firstly preprocessing an original state measurement value of a current moment measured by a platform sensor to obtain a state measurement value of the current moment with higher accuracy, so that the accuracy of the obtained state target value of the platform at the current moment is higher based on the state measurement value of the current moment with high accuracy. According to the application, the information measured by the platform sensor is subjected to self-adaptive preprocessing, so that a more accurate state measurement value is obtained, and further, a state target value obtained according to the state measurement value is more accurate, thereby improving the precision of the platform relative navigation information.

As shown in fig. 4, the relative navigation processing method of the data chain provided by the present application is actually an iterative prediction and update process, wherein,、/>Respectively represent the/>Sub-measurement and measurement noise,/>Represents the time of k via the first/>Sub-measure updated state target value,/>Represents the time of k via the first/>The updated state mean square error matrix is sub-measured.

Thus, in one embodiment, after determining the state target value of the platform at the current time, further comprising: determining a covariance matrix of the current moment according to the filtering gain matrix of the current moment and the prediction covariance matrix of the current moment; acquiring measurement information measured by a platform sensor at the next moment, wherein the measurement information comprises an original state measurement value of the platform at the next moment; preprocessing the original state measurement value at the next moment to obtain a state measurement value at the next moment; the preprocessing at least comprises at least one of data validity processing, accuracy processing, noise reduction processing and diagonalization processing; based on the system-level sequential Kalman filtering model, determining a state target value of the platform at the next moment according to the state measurement value at the next moment, the state target value of the platform at the current moment and the covariance matrix at the current moment, so as to be used for platform navigation.

Referring to fig. 5, fig. 5 is a second flowchart of a relative navigation method of a data link according to an embodiment of the present application, which is applied to the electronic device, after step 203, the method further includes:

step 501, determining a covariance matrix of the current moment according to the filtering gain matrix of the current moment and the prediction covariance matrix of the current moment.

Since the predicted covariance matrix at the current time is a predicted covariance matrix, the predicted covariance matrix at the current time needs to be updated to obtain a more accurate covariance matrix at the current time.

(Equation 15), wherein/>Is the covariance matrix of the current moment of the k moment.

Thus, the filtering gain matrix at the current time is to be usedPrediction covariance matrix/>, at current momentSubstituting into the formula 15, calculating to obtain covariance matrix/>。

Step 502, obtaining measurement information measured by a platform sensor at the next moment, where the measurement information includes an original state measurement value of the platform at the next moment.

The measurement information measured by the platform sensor at time k+1 is obtained, and the obtaining method refers to step 201 above, which is not described herein.

Step 503, preprocessing the original state measurement value at the next moment to obtain the state measurement value at the next moment.

The method of preprocessing is referred to step 202 above, and will not be described here.

Step 504, determining a state target value of the platform at the next moment according to the state measurement value at the next moment, the state target value of the platform at the current moment and the covariance matrix at the current moment based on the system-level sequential Kalman filtering model, so as to be used for platform navigation.

Firstly, based on a system state model in a Kalman filtering model, a state predicted value of a platform at the next moment is obtained according to a state target value of the platform at the current moment, and a predicted covariance matrix at the next moment is obtained according to a covariance matrix at the current moment.

As can be seen from the above equation 11, the state target value of the platform at the current time can be calculatedSubstituting the predicted value into the formula 11 to obtain the state predicted value/>, of the platform at the next moment; The covariance matrix/>, according to the current momentSubstituting the predicted covariance matrix into the formula 12 to obtain the predicted covariance matrix/>, of the next momentFor specific methods, refer to step 203 above.

And determining a filter gain matrix at the next moment based on the noise covariance matrix measured at the next moment in the state measurement values at the next moment and the prediction covariance matrix at the next moment.

As can be seen from equation 13 above, the noise covariance matrix is measured at the next timeAnd the predicted covariance matrix/>, at the next momentWith the result in equation 13, the filter gain matrix/>, at the next time, can be obtainedFor specific calculation, please refer to step 203 above.

And finally, determining a state target value of the platform at the next moment according to the state predicted value at the next moment, the filter gain matrix at the next moment and the state measurement value at the next moment.

As can be seen from equation 14 above, the state prediction value at the next time is calculatedFilter gain matrix/>, next momentState measurement value/>, at the next timeSubstituting into the formula 14, the state target value/>, at the next moment, of the platform can be calculatedFor specific calculation, please refer to step 203 above.

Fig. 6 is a positioning result diagram of a conventional method according to an embodiment of the present application, and fig. 7 is a positioning result diagram of a method according to an embodiment of the present application. As can be seen from comparison of fig. 6 and fig. 7, the fluctuation of the navigation positioning result obtained by the conventional method is larger, the fluctuation of the navigation positioning result obtained by the method is smaller, the east positioning result obtained by the method is improved from 54m to 14m (CEP), and the north positioning result is improved from 47m to 10m (CEP) in precision. Therefore, the state target value obtained based on the method is more accurate and has higher reliability.

The embodiment of the application provides a relative navigation method of a data chain, which can predict and obtain a state predicted value of a current moment according to a state target value of the last moment, and update the state predicted value of the current moment according to a filter gain matrix of the current moment to obtain a more accurate state target value of the current moment; and predicting the state target value at the next moment according to the state target value at the current moment to obtain a state predicted value at the next moment, updating the state predicted value at the next moment according to the filter gain matrix at the next moment to obtain a more accurate state target value at the next moment, and iteratively updating to obtain the state target values at all the moments, wherein the accuracy of the state target values is improved along with the increase of the number of times of iterative updating.

As shown in fig. 8, fig. 8 is a third flow chart of a relative navigation method of a data link according to an embodiment of the present application, where the relative navigation method of a data link of the present application includes the following steps:

And step 1, performing sensor information on-line sensing on the navigation information of the local platform. The online sensing comprises classifying navigation information of the local sensor, judging data validity according to types, giving different weights to valid data, and improving the utilization rate of measurement information.

Step 1.1, classifying the received navigation information of the local platform, acquiring sensor signals of different working mechanisms on line in real time, and realizing accurate aggregation in a time-space domain. According to different signals, the sensor types such as satellite navigation, inertial navigation, combined navigation, takang and the like are distinguished.

And 1.2, judging the validity of the information. The validity of information such as latitude, longitude, altitude, speed, roll angle, pitch angle, heading angle and the like is judged by adopting a logical value function (logistic function, a mathematical function expressed by Boolean algebra, the satisfied condition is 1, and the unsatisfied condition is 0). For a specific method of judging the validity of the data, please refer to the above.

And step 1.3, marking the processed information. And selecting corresponding sensor error characteristics according to different sensor information, and giving corresponding weight values according to actual use environments. Weight distribution is carried out on the same type of sensor, for example, the positioning results given by different positioning sensors are given out simultaneously with the user ranging precision factor N, then the user ranging precision URA is estimated by the following formula according to the value of N, and then the user ranging precision URA is estimated according to the value of NThe weight is calculated according to the following formula, and then the accuracy processing is carried out on the original state measurement value according to the weight, and the detailed method of the accuracy processing is referred to above.

And 2, performing reliability processing on the sensor navigation information obtained in the step 1.

Step 2.1, fig. 9 is a system overall process flow diagram, wherein,Representing a system noise vector with respect to a time parameter t,/>Representing a system state vector with respect to a time parameter t,/>Represents a deterministic measurement matrix with respect to the time parameter t,Representing measurement noise with respect to a time parameter t,/>Representing a measurement value for the time parameter t. Firstly, an independent sensor measurement information preprocessing filter is established, the preprocessing filter is established according to a standard Kalman filter, zero-mean Gaussian white noise is processed, and measurement noise is reflected in the Kalman preprocessing filter to be a measurement noise covariance matrix R _k to take a value.

And 2.2, inputting the sensor navigation information into a preprocessing filter. And setting different measured values of the fading factors b according to actual conditions, and carrying out parameter adjustment on the pre-filter.

And 2.3, filtering, noise reduction and optimization are carried out on the navigation information by using a preprocessing filter.

The reliability processing of the sensor navigation information in the step 2 is the noise reduction processing in the preprocessing above, and will not be described herein.

And 3, performing high-reliability sequential filtering extrapolation and calculation on the relative navigation system.

The extrapolation and resolution of the sequential filtering with high reliability for the relative navigation system may be explained in detail above with reference to step 203, which is not described in detail herein.

On the basis of the method of the foregoing embodiment, this embodiment will be further described from the perspective of a relative navigation device of a data link, referring to fig. 10, fig. 10 specifically illustrates a schematic structural diagram of a relative navigation device of a data link provided by the embodiment of the present application, and the relative navigation device 1000 of a data link may include:

a first obtaining module 1001, configured to obtain measurement information of a platform sensor at a current moment, where the measurement information includes an original state measurement value of the platform at the current moment;

A first preprocessing module 1002, configured to preprocess an original state measurement value at a current time to obtain a state measurement value at the current time; the preprocessing at least comprises at least one of data validity processing, accuracy processing, noise reduction processing and diagonalization processing;

A first determining module 1003, configured to determine, based on a system-level sequential kalman filter model, a state target value of the platform at a current time, according to the state measurement value at the current time, a state target value of the platform at a last time, and a covariance matrix of the last time, for the platform navigation.

In some embodiments of the present application, the first determining module 1003 further includes:

The first determining submodule is used for obtaining a state predicted value of the platform at the current moment according to a state target value of the platform at the last moment based on a system state model in the Kalman filtering model and obtaining a predicted covariance matrix at the current moment according to a covariance matrix at the last moment;

The second determining submodule is used for determining a filtering gain matrix at the current moment according to the measurement noise covariance matrix at the current moment in the state measurement values at the current moment and the prediction covariance matrix at the current moment based on the system measurement model in the Kalman filtering model;

And the third determining submodule is used for determining a state target value of the platform at the current moment according to the state predicted value of the current moment, the filtering gain matrix at the current moment and the state measurement value at the current moment.

In some embodiments of the present application, the first determining module 1003 further includes, after:

The second determining module is used for determining a covariance matrix of the current moment according to the filtering gain matrix of the current moment and the prediction covariance matrix of the current moment;

The second acquisition module is used for acquiring measurement information measured by the platform sensor at the next moment, wherein the measurement information comprises an original state measurement value of the platform at the next moment;

the second preprocessing module is used for preprocessing the original state measurement value at the next moment to obtain the state measurement value at the next moment; the preprocessing at least comprises at least one of data validity processing, accuracy processing, noise reduction processing and diagonalization processing;

And the third determining module is used for determining the state target value of the platform at the next moment according to the state measurement value of the next moment, the state target value of the platform at the current moment and the covariance matrix of the current moment based on the system-level sequential Kalman filtering model, so as to be used for the navigation of the platform.

In addition, in order to better implement the relative navigation method of the data link in the embodiment of the present application, the embodiment of the present application further provides an electronic device, referring to fig. 11, fig. 11 shows a schematic structural diagram of the electronic device in the embodiment of the present application, and specifically, the electronic device provided in the embodiment of the present application includes a processor 1101, where the processor 1101 is configured to implement steps of the relative navigation method of the data link in any embodiment corresponding to fig. 1 to 9 when executing a computer program stored in the memory 1102; or the processor 1101 is configured to implement the functions of the respective modules in the corresponding embodiment of fig. 10 when executing the computer program stored in the memory 1102.

By way of example, a computer program may be partitioned into one or more modules/units that are stored in the memory 1102 and executed by the processor 1101 to accomplish an embodiment of the present application. One or more of the modules/units may be a series of computer program instruction segments capable of performing particular functions to describe the execution of the computer program in a computer device.

Electronic devices may include, but are not limited to, processor 1101, memory 1102. It will be appreciated by those skilled in the art that the illustrations are merely examples of electronic devices, and are not limiting of electronic devices, and may include more or fewer components than shown, or may combine some components, or different components, e.g., electronic devices may also include input and output devices, network access devices, buses, etc., with the processor 1101, memory 1102, input and output devices, network access devices, etc. being connected by buses.

The Processor 1101 may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (DIGITAL SIGNAL Processor, DSP), application SPECIFIC INTEGRATED Circuit (ASIC), off-the-shelf Programmable gate array (Field-Programmable GATE ARRAY, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is a control center for an electronic device, with various interfaces and lines connecting various parts of the overall electronic device.

The memory 1102 may be used to store computer programs and/or modules, and the processor 1101 implements various functions of the computer device by running or executing the computer programs and/or modules stored in the memory 1102 and invoking data stored in the memory 1102. The memory 1102 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, application programs required for at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, video data, etc.) created according to the use of the electronic device, and the like. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart memory card (SMART MEDIA CARD, SMC), secure Digital (SD) card, flash memory card (FLASH CARD), at least one disk storage device, flash memory device, or other volatile solid-state storage device.

It will be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the above-described determining device for delivery address selection, electronic device and corresponding units thereof may refer to the description of the relative navigation method of the data link in any embodiment corresponding to fig. 1 to 9, and will not be repeated herein.

Those of ordinary skill in the art will appreciate that all or a portion of the steps of the various methods of the above embodiments may be performed by instructions, or by instructions controlling associated hardware, which may be stored in a computer-readable storage medium and loaded and executed by a processor.

To this end, an embodiment of the present application provides a computer readable storage medium, in which a plurality of instructions capable of being loaded by a processor are stored, so as to execute steps in a relative navigation method of a data link in any embodiment of the present application as shown in fig. 1 to 9, and specific operations may refer to descriptions of the relative navigation method of the data link in any embodiment of the present application as shown in fig. 1 to 9, which are not repeated herein.

Wherein the computer-readable storage medium may comprise: read Only Memory (ROM), random access Memory (RAM, random Access Memory), magnetic or optical disk, and the like.

Since the instructions stored in the computer readable storage medium can execute the steps in the relative navigation method of the data link according to any embodiment of fig. 1 to 9, the beneficial effects of the relative navigation method of the data link according to any embodiment of fig. 1 to 9 can be achieved, which are detailed in the foregoing description and are not repeated herein.

The above description is provided in detail of a relative navigation method, device, electronic equipment and storage medium of a data link provided by the embodiments of the present application, and specific examples are applied to illustrate the principles and embodiments of the present application, where the above description of the embodiments is only for helping to understand the method and core ideas of the present application; meanwhile, as those skilled in the art will vary in the specific embodiments and application scope according to the ideas of the present application, the present description should not be construed as limiting the present application in summary.

Claims

1. A method of relative navigation of a data link, comprising:

preprocessing the original state measurement value at the current moment to obtain a state measurement value at the current moment; the preprocessing comprises noise reduction processing and other processing methods, wherein the other processing methods comprise any one or any combination of data validity processing, accuracy processing and diagonalization processing;

The noise reduction process includes:

Updating the original measurement noise covariance matrix according to the maximum value and the minimum value of the original measurement noise covariance matrix of the original state measurement value at the current moment and the magnitude relation of the measurement prediction noise at the current moment to obtain the state measurement value at the current moment;

Based on a system-level sequential Kalman filtering model, determining a state target value of the platform at the current moment according to the state measurement value at the current moment, the state target value of the platform at the last moment and a covariance matrix at the last moment, so as to be used for navigation of the platform;

the updating of the original measurement noise covariance matrix according to the maximum value and the minimum value of the original measurement noise covariance matrix of the original state measurement value at the current moment and the magnitude relation of the measurement prediction noise at the current moment to obtain the state measurement value at the current moment comprises the following steps:

If the measurement prediction noise at the current moment is not greater than the minimum value of the original measurement noise covariance matrix, updating the original measurement noise covariance matrix according to the self-adaptive factor at the current moment to obtain the measurement noise covariance matrix at the current moment and the state measurement value at the current moment;

the determining, based on the system-level sequential kalman filter model, the state target value of the platform at the current moment according to the state measurement value at the current moment, the state target value of the platform at the last moment, and the covariance matrix at the last moment, includes:

2. The method of relative navigation of a data link of claim 1, wherein the measurement information carries a sensor class identification, and the data validity process comprises:

3. The method of relative navigation of a data chain of claim 1, wherein the platform sensor comprises at least a first sensor and a second sensor, the first sensor and the second sensor being of the same class of sensors, the accuracy process comprising:

4. The method of claim 1, wherein the measurement information includes sub-measurement information of a plurality of sensors, and the raw state measurement value at the current time includes a plurality of raw state sub-measurement values; the diagonalization process includes:

5. A relative navigation device of a data link, comprising:

The first preprocessing module is used for preprocessing the original state measurement value at the current moment to obtain the state measurement value at the current moment; the preprocessing comprises noise reduction processing and other processing methods, wherein the other processing methods comprise any one or any combination of data validity processing, accuracy processing and diagonalization processing;

The first determining module is used for determining a state target value of the platform at the current moment according to the state measurement value at the current moment, the state target value of the platform at the last moment and a covariance matrix at the last moment based on a system-level sequential Kalman filtering model, so as to be used for the navigation of the platform;

the first preprocessing module is specifically configured to calculate, according to the prediction covariance matrix at the current time, the original state measurement value at the current time, and the measurement prediction value at the current time, obtain measurement prediction noise at the current time;

the first determining module further includes:

6. An electronic device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps in the relative navigation method of a data link according to any one of claims 1 to 4 when the computer program is executed.

7. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps in a relative navigation method of a data link according to any one of claims 1 to 4.